Paper: Discriminative Learning for Joint Template Filling

ACL ID P12-1089
Title Discriminative Learning for Joint Template Filling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012

This paper presents a joint model for tem- plate filling, where the goal is to automati- cally specify the fields of target relations such as seminar announcements or corporate acqui- sition events. The approach models mention detection, unification and field extraction in a flexible, feature-rich model that allows for joint modeling of interdependencies at all lev- els and across fields. Such an approach can, for example, learn likely event durations and the fact that start times should come before end times. While the joint inference space is large, we demonstrate effective learning with a Perceptron-style approach that uses simple, greedy beam decoding. Empirical results in two benchmark domains demonstrate consis- tently strong performance on both mention de- tection and template filli...